A Modular Framework for 2D/3D and Multi-Modal Segmentation with Joint Super-Resolution
Abstract
A versatile multi-image segmentation framework for 2D/3D or multi-modal segmentation is introduced in this paper with possible application in a wide range of machine vision problems. The framework performs a joint segmentation and super-resolution to account for images of unequal resolutions gained from different imaging sensors. This allows to combine high resolution details of one modality with the distinctiveness of another modality. A set of measures is introduced to weight measurements according to their expected reliability and it is utilized in the segmentation as well as the super-resolution. The approach is demonstrated with different experimental setups and the effect of additional modalities as well as of the parameters of the framework are shown.
Cite
Text
Langmann et al. "A Modular Framework for 2D/3D and Multi-Modal Segmentation with Joint Super-Resolution." European Conference on Computer Vision, 2012. doi:10.1007/978-3-642-33868-7_2Markdown
[Langmann et al. "A Modular Framework for 2D/3D and Multi-Modal Segmentation with Joint Super-Resolution." European Conference on Computer Vision, 2012.](https://mlanthology.org/eccv/2012/langmann2012eccv-modular/) doi:10.1007/978-3-642-33868-7_2BibTeX
@inproceedings{langmann2012eccv-modular,
title = {{A Modular Framework for 2D/3D and Multi-Modal Segmentation with Joint Super-Resolution}},
author = {Langmann, Benjamin and Hartmann, Klaus and Loffeld, Otmar},
booktitle = {European Conference on Computer Vision},
year = {2012},
pages = {12-21},
doi = {10.1007/978-3-642-33868-7_2},
url = {https://mlanthology.org/eccv/2012/langmann2012eccv-modular/}
}